class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: A faster way to fit/transform.
  
  This methods produces exactly the same result as calling `fit(X)` followed
  by `transform(X)`. It is however much faster, as it avoids a matrix multiplication
  between the input data and the right eigenvectors. The row coordinates are instead obtained
  directly from the left eigenvectors.
docstr_tok: ["A","faster","way","to","fit\/transform",".","This","methods","produces","exactly","the","same","result","as","calling","`","fit","(","X",")","`","followed","by","`","transform","(","X",")","`",".","It","is","however","much","faster",",","as","it","avoids","a","matrix","multiplication","between","the","input","data","and","the","right","eigenvectors",".","The","row","coordinates","are","instead","obtained","directly","from","the","left","eigenvectors","."]
code: def fit_transform(self, X, y=None, as_array=False):
      """A faster way to fit/transform.
  
      This methods produces exactly the same result as calling `fit(X)` followed
      by `transform(X)`. It is however much faster, as it avoids a matrix multiplication
      between the input data and the right eigenvectors. The row coordinates are instead obtained
      directly from the left eigenvectors.
  
      """
      self._check_input(X)
      self.fit(X)
      rc = self.row_coordinates(X)
      return rc.to_numpy() if as_array else rc
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 8 for calling the PCA obj's underlying member method code with example usage: obj.fit_transform(X, y, as_array) and returns: unknown